A novel channel estimation method: blending correlation and least-squares based approaches

Ozen,; Zoltowski,; Fimoff,

doi:10.1109/icassp.2002.1005138

Cited by 5 publications

(4 citation statements)

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“…For instance the 8-VSB digital TV system has 728 training symbols, whereas the delay spreads of the terrestrial channels have been observed to be at least 400-500 symbols long. 5,6 The a-BLUE algorithm can be used as an initializer to the BLUE iterations, 7 or as a stand-alone alternative 8 approach that produces results of nearly the same quality as the results produced by the BLUE algorithm while at the same time requiring much less computational complexity (i.e., requiring about the same number of multiplications necessary to implement ordinary least squares) and having storage requirements similar to that of ordinary least squares.…”

Section: Introductionmentioning

confidence: 98%

Approximate best linear unbiased channel estimation for multi-antenna frequency selective channels with applications to digital TV systems

Özen

Pladdy²,

Nerayanuru³

et al. 2004

SPIE Proceedings

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We provide an iterative and a non-iterative channel impulse response (CIR) estimation algorithm for communication receivers with multiple-antenna. Our algorithm is best suited for communication systems which utilize a periodically transmitted training sequence within a continuous stream of information symbols, and the receivers for this particular system are expected work in a severe frequency selective multipath environment with long delay spreads relative to the length of the training sequence. The iterative procedure calculates the (semi-blind) Best Linear Unbiased Estimate (BLUE) of the CIR. The non-iterative version is an approximation to the BLUE CIR estimate, denoted by a-BLUE, achieving almost similar performance, with much lower complexity. Indeed we show that, with reasonable assumptions, a-BLUE channel estimate can be obtained by using a stored copy of a pre-computed matrix in the receiver which enables the use of the initial CIR estimate by the subsequent equalizer tap weight calculator. Simulation results are provided to demonstrate the performance of the novel algorithms for 8-VSB ATSC Digital TV system. We also provide a simulation study of the robustness of the a-BLUE algorithm to timing and carrier phase offsets.

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Section: Introductionmentioning

confidence: 98%

Approximate best linear unbiased channel estimation for multi-antenna frequency selective channels with applications to digital TV systems

Özen

Pladdy²,

Nerayanuru³

et al. 2004

SPIE Proceedings

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“…is the M -ary complex-valued training sequence A ⊂ C [13]- [15]. {a k } denotes the first N known (training) symbols within a frame of length N , and {d k } denotes the remaining N − N random data within the frame.…”

Section: Baseband Data Transmission Modelmentioning

confidence: 99%

Semiblind BLUE Channel Estimation With Applications to Digital Television

Pladdy¹,

OzenOzen²,

Nerayanuru

et al. 2006

IEEE Trans. Veh. Technol.

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Abstract-A semiblind iterative algorithm to construct the best linear unbiased estimate (BLUE) of the channel impulse response (CIR) vector h for communication systems that utilize a periodically transmitted training sequence within a continuous stream of information symbols is devised. The BLUE CIR estimate for the general linear model y = Ah + w, where w is the correlated noise, is given by the Gauss-Markoff theorem. The covariance matrix of the correlated noise, which is denoted by C(h), is a function of the channel that is to be identified. Consequently, an iteration is used to give successive approximations h (k) , k = 0, 1, 2, . . . to h BLUE , where h (0) is an initial approximation given by the correlation processing, which exists at the receiver for the purpose of frame synchronization. A function F (h) for which h BLUE is a fixed point is defined. Conditions under which h BLUE is the unique fixed point and for which the iteration proposed in the algorithm converges to the unique fixed point h BLUE are given. The proofs of these results follow broadly along the lines of Banach fixed-point theorems.

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“…Channel can be estimated using training sequences [17], [18], but this issue is not considered in this paper. The signal model and the detectors in this paper are derived for synchronous multipath channels, however, if the user delays are known or estimated, the asynchronous multipath channels can be directly modelled in a similar format as the synchronous channels, and hence the developed adaptive multiuser detectors can also be applied directly.…”

Section: Introductionmentioning

confidence: 99%

Robust multiuser detection using Kalman filter and windowed projection approximation subspace tracking algorithm

Zhou¹,

Xiao

2011

IET Commun.

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A novel channel estimation method: blending correlation and least-squares based approaches

Cited by 5 publications

References 0 publications

Approximate best linear unbiased channel estimation for multi-antenna frequency selective channels with applications to digital TV systems

Approximate best linear unbiased channel estimation for multi-antenna frequency selective channels with applications to digital TV systems

Semiblind BLUE Channel Estimation With Applications to Digital Television

Robust multiuser detection using Kalman filter and windowed projection approximation subspace tracking algorithm

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